Modeling of Feature Selection Based on Random Forest Algorithm and Pearson Correlation Coefficient

This paper establishes a feature selection model to selects 20 molecular descriptors of compounds with the most significant influence on biological activity. Random forest algorithm was used to calculate the correlation between molecular descriptors and pIC50 values of biological activity. In this w...

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Bibliographic Details
Published in:Journal of physics. Conference series Vol. 2219; no. 1; pp. 12046 - 12054
Main Authors: Mei, Kai, Tan, Meifang, Yang, Zhihui, Shi, Shaoyue
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.04.2022
Subjects:
Algorithms
Biological activity
Biological effects
Correlation coefficients
Feature selection
Independent variables
Pearson correlation coefficient
Physics
Random forest algorithm
ISSN:1742-6588, 1742-6596
Online Access:Get full text
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Summary:This paper establishes a feature selection model to selects 20 molecular descriptors of compounds with the most significant influence on biological activity. Random forest algorithm was used to calculate the correlation between molecular descriptors and pIC50 values of biological activity. In this way, the top 26 molecular descriptors with high correlation were screened out. The Pearson correlation coefficient was used to analyze the 26 molecular descriptors just selected and eliminate the variables with high correlation between the independent variables. By consulting literature, the parameters such as MlogP, XlogP and TopoPSA in the selected molecular descriptors were found that had a prominent effect on the biological activity, indicating that the screening methods and results of the 20 molecular descriptors were reasonable.
Bibliography:ObjectType-Article-1
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ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2219/1/012046